Search result diversification via data fusion

Shengli Wu, Chunlan Huang

Abstract: In recent years, researchers have investigated search result diversification through a variety of approaches. In such situations, information retrieval systems need to consider both aspects of relevance and diversity for those retrieved documents. On the other hand, previous research has demonstrated that data fusion is useful for improving performance when we are only concerned with relevance. However, it is not clear if it helps when both relevance and diversity are both taken into consideration. In this short paper, we propose a few data fusion methods to try to improve performance when both relevance and diversity are concerned. Experiments are carried out with 3 groups of top-ranked results submitted to the TREC web diversity task. We find that data fusion is still a useful approach to performance improvement for diversity as for relevance previously.